1. Field
Certain aspects of the present disclosure generally relate to neural system engineering and, more particularly, to systems and methods for designing and operating a neural network using stochastic delay plasticity.
2. Background
An artificial neural network, which may comprise an interconnected group of artificial neurons (i.e., neuron models), is a computational device or represents a method to be performed by a computational device. Artificial neural networks may have corresponding structure and/or function in biological neural networks. However, artificial neural networks may provide innovative and useful computational techniques for certain applications in which traditional computational techniques are cumbersome, impractical, or inadequate. Because artificial neural networks can infer a function from observations, such networks are particularly useful in applications where the complexity of the task or data makes the design of the function by conventional techniques burdensome.
Spike-timing dependent delay plasticity is a technique to alter the time at which incoming information from a presynaptic neuron arrives at or is updated in a postsynaptic neuron. In many cases, the function of delay plasticity is to cause temporally separated events to arrive at the postsynaptic neuron at the same time, which increases the likelihood that the neuron will fire an action potential. Delay plasticity is typically implemented by changing the delay of the synapse (or connection) between pairs of pre- and postsynaptic neurons. Spike-timing dependent delay plasticity is a special case of delay plasticity where the change in delay is determined by the time difference between the pre- and post-synaptic spike times. In past implementations, the delay change was deterministic; meaning that for a given time difference between the pre- and postsynaptic neurons the delay change was a pre-determined value. Implementations typically rely on a positive delay change when the pre-fires before the postsynaptic neuron and a negative delay change in the reverse case. While this approach works well when the postsynaptic neuron fires in the middle of a group of presynaptic spikes, invoking both positive and negative delay changes, when the postsynaptic spike fires after a group of presynaptic spikes, only positive delay changes are invoked. This produces ever increasing delays that can saturate or add unnecessary delay to the system (e.g., producing synaptic delays of 25, 27, and 29 when delays of 1, 3, and 5 would provide the same functionality). This gratuitous delay occurs because there is no mechanism to minimize the overall delay after the presynaptic spikes have been clustered together.